Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add meltedinhex/analyst-ai-pack --skill analyzing-compiled-python-malwaregit clone --depth 1 https://github.com/meltedinhex/analyst-ai-packWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/meltedinhex/analyst-ai-pack/analyzing-compiled-python-malware)<a href="https://agentmods.dev/skills/meltedinhex/analyst-ai-pack/analyzing-compiled-python-malware"><img src="https://agentmods.dev/badge/skills/meltedinhex/analyst-ai-pack/analyzing-compiled-python-malware/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/meltedinhex/analyst-ai-pack/analyzing-compiled-python-malware"><img src="https://agentmods.dev/badge/skills/meltedinhex/analyst-ai-pack/analyzing-compiled-python-malware.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00076 | $0.00735 |
| Opus 5.5 | $0.00030 | $0.00294 |
| Sonnet 5.5 | $0.00015 | $0.00147 |
| Haiku 4.5 | $0.00008 | $0.00073 |
Grade A, and why
analyzing-compiled-python-malware scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 90 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Analyzing Compiled Python Malware
When to Use
- You have an executable that is actually a frozen Python app (PyInstaller, py2exe, cx_Freeze) and need to identify the packer and locate the embedded Python modules.
- You want to extract
.pycfiles for decompilation.
Do not use this to run the executable — it identifies and locates the embedded archive
statically. Decompile extracted .pyc in an isolated environment.
Prerequisites
- The frozen executable (read inertly).
Safety & Handling
- Read bytes statically; treat extracted modules as malicious until reviewed.
Workflow
Step 1: Detect the packer
python scripts/analyst.py detect sample.exe
Looks for PyInstaller markers (pyi-, PYZ-00.pyz, the MEI CArchive cookie MEI\014\013\012\013\016),
py2exe (PYTHONSCRIPT, zipfile.zip), and embedded python3x.dll references.
Step 2: Locate the embedded archive
Find the CArchive cookie near the end of the file and report the offset and the embedded Python
version string (python3.x).
Step 3: Extract and decompile
Use a PyInstaller extractor to dump the archive, then decompile .pyc (matching the detected
Python version) for source recovery.
Step 4: Analyze the source
Review the recovered Python for C2, persistence, and capability; map to ATT&CK.
Validation
- The packer is identified by its specific marker, not just the presence of Python strings.
- The CArchive cookie offset and Python version are reported when PyInstaller is present.
- Findings distinguish the bootloader stub from the embedded Python payload.
Pitfalls
.pycversion mismatch breaking decompilation — match the interpreter version.- Stripped/obfuscated bytecode (e.g., custom magic) needing header repair before decompiling.
- Encrypted PYZ archives (PyInstaller
--key) requiring the key.
References
- See
references/api-reference.mdfor the detector. - PyInstaller archive and Python bytecode references (linked in frontmatter).
What ships with it
3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago First seen · 90 lines · 76 tokens per session scan A c710a0ae28bc
analyzing-compiled-python-malware is a skill published in the GitHub repository meltedinhex/analyst-ai-pack (22 stars, last pushed 3mo ago), licensed Apache-2.0. It adds 76 tokens to every session and 735 once invoked, about $0.0003 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
analyzing-malicious-pdf-with-peepdf
Perform static analysis of malicious PDF documents using peepdf, pdfid, and pdf-parser to extract embedded JavaScript, shellcode, and suspicious objects.
analyzing-malicious-pdf-with-peepdf
Perform static analysis of malicious PDF documents using peepdf, pdfid, and pdf-parser to extract embedded JavaScript, shellcode, and suspicious objects.
analyzing-malicious-pdf-with-peepdf
Perform static analysis of malicious PDF documents using peepdf, pdfid, and pdf-parser to extract embedded JavaScript, shellcode, and suspicious objects.
analyzing-golang-malware-with-ghidra
Reverse engineer Go-compiled malware in Ghidra by parsing Go buildinfo and pclntab structures, recovering stripped/obfuscated function names (e.g. via GoResolver), and extracting embedded module/dependency strings and types from Go binaries. Use when analyzing a Go-language malware sample, deobfuscating a…
analyzing-malicious-pdf-with-peepdf
Perform static analysis of malicious PDF documents using peepdf, pdfid, and pdf-parser to extract embedded JavaScript, shellcode, and suspicious objects. Use when triaging a suspicious PDF attachment from a phishing email, analyzing a PDF-based exploit document, or building detection signatures for weaponized PDF…
cardputer-buddy
Iterate on the Cardputer-Adv MicroPython app bundle (Claude Buddy, Snake, Hello) after the device is already provisioned via m5-onboard. Use when the user wants to add a new app, push a single changed .py without re-flashing, watch device serial logs, or run a one-shot REPL command. Trigger on "add an app", "push to…